
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "36"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 95 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 91 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.396757	-0.983352	1.398109	1.097970	-0.567653	0.071625	-0.220707	0.378091	0.0806	0.0752	0.0073	1.250651	1.251320
2459593	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.090038	-1.452553	2.180569	0.707498	0.003315	0.422044	-0.906496	-0.597991	0.0913	0.0857	0.0100	1.229928	1.227971
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.686341	-0.675757	0.129744	-0.498762	-0.166210	0.557392	-1.896426	-0.058871	0.0280	0.0276	0.0006	nan	nan
2459591	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.072171	-1.724707	2.769801	1.499540	-0.059621	0.263208	-1.211819	-0.334426	0.0905	0.0888	0.0118	1.192252	1.191909
2459590	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.158933	-1.347746	2.336858	0.873845	-0.028968	-0.093714	-0.831394	-0.013382	0.0938	0.0870	0.0121	1.235178	1.232897
2459589	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.148383	-1.615386	1.869355	0.698623	-0.030655	-0.485133	0.737673	-0.021853	0.0859	0.0755	0.0095	1.229216	1.228998
2459588	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.599867	-0.895122	2.725878	0.764442	-0.476203	-0.129729	-0.380518	-0.435670	0.0518	0.0656	0.0042	1.218426	1.216398
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	0.343004	-1.389096	5.054516	2.518009	2.239540	0.912839	-0.946688	0.328935	nan	nan	nan	4.742597	1.090199
2459586	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.158488	-0.954925	0.739154	-0.351054	-0.258026	-0.830762	-1.149024	-0.380678	0.0930	0.0798	0.0121	1.217626	1.218888
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.802090	-0.828779	0.678121	-0.505224	0.135268	-0.913328	-1.697221	-0.336008	0.0302	0.0306	0.0014	0.000000	0.000000
2459584	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.376645	0.585070	7.172106	2.653824	18.816724	9.900425	-0.293521	0.453148	0.0793	0.0710	0.0106	3.381914	0.849578
2459583	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.933695	-0.527798	3.769343	1.600324	1.145992	-0.264212	-1.113197	-0.469718	0.0939	0.0819	0.0128	1.329753	1.333241
2459582	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.898275	-0.703074	3.650232	1.425862	1.902764	0.258491	-0.724338	-0.528358	0.0559	0.0652	0.0043	1.261649	1.265232
2459581	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.154534	-0.537146	3.311513	1.772100	1.325073	0.478446	-1.090405	-0.666881	0.0907	0.0763	0.0116	1.186489	1.195325
2459580	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.170357	-0.281788	4.252675	1.894394	2.144775	0.315500	-0.923492	0.358891	0.0578	0.0674	0.0051	1.304690	1.306036
2459579	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.256911	-0.378617	4.883062	2.092113	1.900496	0.416754	-1.123485	0.305593	0.0910	0.0677	0.0105	1.241794	1.242111
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.004153	0.681916	0.618808	0.703753	-0.722906	1.035409	-0.542257	-0.548295	0.0284	0.0277	0.0006	nan	nan
2459577	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459576	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.853374	-0.770681	2.304442	0.834871	1.814416	-0.550388	-0.781688	-0.530361	0.0992	0.0717	0.0119	1.203451	1.206529
2459575	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.904022	-1.072129	2.472656	0.782053	1.533783	-0.271435	2.005543	0.316752	0.0952	0.0701	0.0084	0.000000	0.000000
2459574	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.757412	-0.852422	2.115586	0.587683	1.437947	0.207741	-1.057179	-0.753797	0.0921	0.0743	0.0112	1.223500	1.229764
2459573	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.449064	-0.654995	5.096201	2.405627	2.003915	-0.306872	-1.277672	-0.197094	0.0937	0.0756	0.0120	0.000000	0.000000
2459572	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.860545	-0.988615	2.354310	0.838013	1.142991	-0.351539	-1.139824	1.357606	0.0979	0.0824	0.0125	1.323349	1.327963
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.093729	0.657363	0.713644	0.626064	-0.159721	0.693578	-0.602076	-0.449061	0.0292	0.0283	0.0009	nan	nan
2459570	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.243115	0.087550	0.265080	0.142483	0.022851	-0.022851	-1.230160	-0.832033	0.0801	0.0836	0.0117	1.214242	1.213187
2459569	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.244869	-0.899521	2.621250	1.092997	1.293038	0.006159	1.776503	0.691615	0.0915	0.0726	0.0069	1.264232	1.270583
2459566	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.986483	-0.694749	3.414373	1.521998	1.718998	0.511007	-1.230865	-0.388151	0.0877	0.0756	0.0108	-0.000000	-0.000000
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	-0.458228	-0.458228	-0.411532	-0.411532	-0.677191	-0.677191	-0.808887	-0.808887	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.029774	-0.951693	-2.264301	-1.545835	4.586210	1.222683	-0.514188	0.975913	0.0915	0.0804	0.0090	1.212389	1.217878
2459563	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.136778	-0.598240	1.069571	0.315278	0.210443	0.712377	-0.716145	1.279786	0.0955	0.0837	0.0104	1.156012	1.153183
2459562	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.728335	-1.447307	-2.476015	-1.449305	-1.401962	-0.083388	-0.718534	0.199902	0.0999	0.0850	0.0106	1.225657	1.231436
2459561	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.491820	-1.646815	-2.412782	-1.587369	-1.162387	0.004544	-1.170719	0.472605	0.0849	0.0751	0.0066	1.262714	1.272530
2459560	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.675949	-1.309921	-2.407737	-1.574691	-0.962617	-0.173947	-0.629017	0.240993	0.0942	0.0779	0.0109	1.266448	1.276066
2459559	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.594872	-1.466569	-2.341179	-1.729365	-1.246520	-0.286281	-0.717785	-0.473690	0.0867	0.0630	0.0076	1.217535	1.221413
2459558	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.212445	-0.396882	4.261406	1.640861	1.646563	-0.265290	-0.950706	0.074700	0.0951	0.0803	0.0093	1.215157	1.216192
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0295	0.0284	0.0009	nan	nan
2459556	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.288710	-0.309611	4.443470	1.648870	1.884656	0.064342	-1.600455	0.286458	0.0760	0.0720	0.0049	1.195322	1.194213
2459554	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0935	0.0779	0.0096	nan	nan
2459553	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0730	0.0613	0.0044	nan	nan
2459552	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.235106	-0.263527	5.893707	3.452512	3.635224	0.291238	-0.861321	-0.516960	0.0835	0.0697	0.0090	10.650549	8.694952
2459551	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.975301	-1.273548	-2.889691	-1.924033	-1.844858	-0.475292	-0.809596	-0.256375	0.0836	0.0827	0.0098	1.222490	1.226173
2459550	digital_ok	-	98.35%	98.35%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0465	0.0466	0.0012	nan	nan
2459549	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.184586	-0.109931	4.089567	1.822460	2.135293	0.321830	-0.981635	-0.055804	0.0820	0.0773	0.0097	1.204293	1.203906
2459542	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.795837	-0.386813	2.996621	1.751947	2.944419	0.580256	-0.832234	0.518261	0.0881	0.0802	0.0097	1.206385	1.204818
2459541	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.747935	-0.856250	5.339804	2.393069	2.796455	1.126064	1.305105	0.628474	0.0957	0.0798	0.0102	1.236867	1.233290
2459540	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.420484	-1.326095	-1.207565	-1.640715	-2.412177	-1.592340	-1.254082	-0.494189	0.0925	0.0752	0.0121	1.225235	1.221874
2459536	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.288711	-0.476313	7.008460	3.995263	5.372888	1.750769	-0.288570	0.734509	0.0909	0.0761	0.0130	1.156993	1.153868
2459535	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.159010	-0.961558	-1.365640	-1.527331	-2.704989	-1.460572	-0.972264	-0.387270	0.1125	0.0850	0.0190	1.263265	1.258411
2459534	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.139243	-1.138112	-2.261438	-1.508379	-1.698134	-0.866012	-0.570647	0.169871	0.1061	0.0759	0.0101	1.217692	1.214425
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.661579	0.135755	4.399465	2.095251	1.959929	0.983327	-1.308882	0.401044	0.0898	0.0752	0.0097	1.229614	1.228391
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.052891	0.177146	5.879834	2.667736	2.950968	1.319523	-1.210708	0.035819	0.0918	0.0745	0.0113	1.261824	1.259153
2459530	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.497672	-1.449648	-2.189137	-1.439578	-1.591294	-0.355265	-0.604327	0.892911	0.1162	0.0777	0.0144	1.274190	1.268300
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.828734	-0.801141	4.835377	2.400062	5.627741	1.642577	-1.078857	0.103485	0.0910	0.0794	0.0059	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	1.925134	-1.495759	5.503757	2.981073	29.079879	29.434814	13.853501	15.660685	nan	nan	nan	1.480896	1.487515
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.011065	0.700029	6.082112	3.246035	0.364557	9.440842	-0.616756	3.485176	0.0910	0.0820	0.0059	1.238075	1.239812
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	1.338764	0.705573	0.342801	0.012843	0.321016	2.256077	-0.623624	-0.094174	0.0317	0.0331	0.0015	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.308317	-0.844706	3.815248	1.373077	2.987127	1.685477	2.488131	0.951811	0.9071	0.9567	0.1815	0.000000	0.000000
2459505	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.358531	-1.301140	4.921500	1.463200	3.103044	4.717749	-0.308719	0.942311	0.8884	0.8533	0.2055	10.202640	8.704844
2459503	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.360788	0.153566	5.343127	2.658107	7.837973	3.851209	-0.629796	-0.616247	0.8479	0.8556	0.4947	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 36 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 36, Node 3:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 36: 2459619

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶